Kaggle NFL 2020 Competition¶

Columns¶

Each row in the file corresponds to a single player's involvement in a single play. The dataset was intentionally joined (i.e. denormalized) to make the API simple. All the columns are contained in one large dataframe which is grouped and provided by PlayId.

GameId - a unique game identifier
PlayId - a unique play identifier
Team - home or away
X - player position along the long axis of the field. See figure below.
Y - player position along the short axis of the field. See figure below.
S - speed in yards/second
A - acceleration in yards/second^2
Dis - distance traveled from prior time point, in yards
Orientation - orientation of player (deg)
Dir - angle of player motion (deg)
NflId - a unique identifier of the player
DisplayName - player's name
JerseyNumber - jersey number
Season - year of the season
YardLine - the yard line of the line of scrimmage
Quarter - game quarter (1-5, 5 == overtime)
GameClock - time on the game clock
PossessionTeam - team with possession
Down - the down (1-4)
Distance - yards needed for a first down
FieldPosition - which side of the field the play is happening on
HomeScoreBeforePlay - home team score before play started
VisitorScoreBeforePlay - visitor team score before play started
NflIdRusher - the NflId of the rushing player
OffenseFormation - offense formation
OffensePersonnel - offensive team positional grouping
DefendersInTheBox - number of defenders lined up near the line of scrimmage, spanning the width of the offensive line
DefensePersonnel - defensive team positional grouping
PlayDirection - direction the play is headed
TimeHandoff - UTC time of the handoff
TimeSnap - UTC time of the snap
Yards - the yardage gained on the play (you are predicting this)
PlayerHeight - player height (ft-in)
PlayerWeight - player weight (lbs)
PlayerBirthDate - birth date (mm/dd/yyyy)
PlayerCollegeName - where the player attended college
Position - the player's position (the specific role on the field that they typically play)
HomeTeamAbbr - home team abbreviation
VisitorTeamAbbr - visitor team abbreviation
Week - week into the season
Stadium - stadium where the game is being played
Location - city where the game is being played
StadiumType - description of the stadium environment
Turf - description of the field surface
GameWeather - description of the game weather
Temperature - temperature (deg F)
Humidity - humidity
WindSpeed - wind speed in miles/hour
WindDirection - wind direction

Field¶

field

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import pandas as pd
import pandas as pd

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train_df = pd.read_csv('nfl/train.csv', dtype={'FieldPosition': str}, low_memory=False)
train_df = pd.read_csv('nfl/train.csv', dtype={'FieldPosition': str}, low_memory=False)

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sorted_data = train_df.sort_values(by=['TimeHandoff'], ascending=[True]);
sorted_data[['PlayId', 'TimeHandoff']].head(50)
sorted_data = train_df.sort_values(by=['TimeHandoff'], ascending=[True]);
sorted_data[['PlayId', 'TimeHandoff']].head(50)

Out[110]:

	PlayId	TimeHandoff
0	20170907000118	2017-09-08T00:44:06.000Z
21	20170907000118	2017-09-08T00:44:06.000Z
20	20170907000118	2017-09-08T00:44:06.000Z
19	20170907000118	2017-09-08T00:44:06.000Z
18	20170907000118	2017-09-08T00:44:06.000Z
17	20170907000118	2017-09-08T00:44:06.000Z
16	20170907000118	2017-09-08T00:44:06.000Z
14	20170907000118	2017-09-08T00:44:06.000Z
13	20170907000118	2017-09-08T00:44:06.000Z
12	20170907000118	2017-09-08T00:44:06.000Z
11	20170907000118	2017-09-08T00:44:06.000Z
15	20170907000118	2017-09-08T00:44:06.000Z
9	20170907000118	2017-09-08T00:44:06.000Z
10	20170907000118	2017-09-08T00:44:06.000Z
2	20170907000118	2017-09-08T00:44:06.000Z
3	20170907000118	2017-09-08T00:44:06.000Z
4	20170907000118	2017-09-08T00:44:06.000Z
1	20170907000118	2017-09-08T00:44:06.000Z
6	20170907000118	2017-09-08T00:44:06.000Z
7	20170907000118	2017-09-08T00:44:06.000Z
8	20170907000118	2017-09-08T00:44:06.000Z
5	20170907000118	2017-09-08T00:44:06.000Z
34	20170907000139	2017-09-08T00:44:27.000Z
43	20170907000139	2017-09-08T00:44:27.000Z
42	20170907000139	2017-09-08T00:44:27.000Z
41	20170907000139	2017-09-08T00:44:27.000Z
40	20170907000139	2017-09-08T00:44:27.000Z
39	20170907000139	2017-09-08T00:44:27.000Z
37	20170907000139	2017-09-08T00:44:27.000Z
36	20170907000139	2017-09-08T00:44:27.000Z
35	20170907000139	2017-09-08T00:44:27.000Z
33	20170907000139	2017-09-08T00:44:27.000Z
38	20170907000139	2017-09-08T00:44:27.000Z
31	20170907000139	2017-09-08T00:44:27.000Z
23	20170907000139	2017-09-08T00:44:27.000Z
24	20170907000139	2017-09-08T00:44:27.000Z
25	20170907000139	2017-09-08T00:44:27.000Z
26	20170907000139	2017-09-08T00:44:27.000Z
22	20170907000139	2017-09-08T00:44:27.000Z
27	20170907000139	2017-09-08T00:44:27.000Z
28	20170907000139	2017-09-08T00:44:27.000Z
29	20170907000139	2017-09-08T00:44:27.000Z
30	20170907000139	2017-09-08T00:44:27.000Z
32	20170907000139	2017-09-08T00:44:27.000Z
55	20170907000189	2017-09-08T00:45:17.000Z
64	20170907000189	2017-09-08T00:45:17.000Z
63	20170907000189	2017-09-08T00:45:17.000Z
62	20170907000189	2017-09-08T00:45:17.000Z
61	20170907000189	2017-09-08T00:45:17.000Z
60	20170907000189	2017-09-08T00:45:17.000Z

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import matplotlib.pyplot as plt

plt.figure(figsize=(10,7))
train_df['Yards'].hist(bins=50, edgecolor='k', alpha=0.7)
plt.title('Distribution of Yards Gained')
plt.xlabel('Yards Gained')
plt.ylabel('Number of Plays')
plt.grid(False)
plt.show()
import matplotlib.pyplot as plt

plt.figure(figsize=(10,7))
train_df['Yards'].hist(bins=50, edgecolor='k', alpha=0.7)
plt.title('Distribution of Yards Gained')
plt.xlabel('Yards Gained')
plt.ylabel('Number of Plays')
plt.grid(False)
plt.show()

No description has been provided for this image

Exploratory Analysis¶

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# @title Filter By PlayId
GameId = 2017090700 # @param {type:"integer"}
# let's start to see how much data we have for a Play
game_data = train_df[train_df['GameId'] == GameId]
game_data['PlayId'].unique().shape
# @title Filter By PlayId
GameId = 2017090700 # @param {type:"integer"}
# let's start to see how much data we have for a Play
game_data = train_df[train_df['GameId'] == GameId]
game_data['PlayId'].unique().shape

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(52,)

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from skimage import io
image = io.imread("https://mrinaldi2.github.io/mkdocs-material-nfl/assets/images/AmFBfield.png")
from skimage import io
image = io.imread("https://mrinaldi2.github.io/mkdocs-material-nfl/assets/images/AmFBfield.png")

Draw Field Positions¶

The image used it was modified starting by the image here

Attribute to the image author

By The original uploader was Xyzzy n at English Wikipedia. - Transferred from en.wikipedia to Commons., CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=2257082

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# List of unique PlayIds for the game
play_ids = game_data['PlayId'].unique()
print(play_ids)
# Plot the first few plays as an example
num_rows = (play_ids.shape[0] // 3) + 1  # Number of plays to plot
num_cols = 3
fig, axes = plt.subplots(num_rows, num_cols, figsize=(30, 126))

for idx, play_id in enumerate(play_ids):
    row = idx // 3
    col = idx % 3
    play_data = game_data[game_data['PlayId'] == play_id]
    yard_line = play_data['YardLine'].iloc[0]
    yards = play_data['Yards'].iloc[0]
    field_position = play_data['FieldPosition'].iloc[0]
    possesion_team = play_data['PossessionTeam'].iloc[0]
    home_team_abbr = play_data['HomeTeamAbbr'].iloc[0]
    if field_position == home_team_abbr:
      absolute_yard_line = 10 + 50 + (50-yard_line)
    else:
      absolute_yard_line = 10 + yard_line
    if (possesion_team == home_team_abbr):
      yards = absolute_yard_line - yards
    else:
      yards = absolute_yard_line + yards
    axes[row, col].imshow(image, extent=[-1, 121, -1, 54])
    axes[row, col].scatter(play_data[play_data['Team'] == 'away']['X'], play_data[play_data['Team'] == 'away']['Y'], c='red', s=100)
    axes[row, col].scatter(play_data[play_data['Team'] == 'home']['X'], play_data[play_data['Team'] == 'home']['Y'], c='blue', s=100)
    axes[row, col].scatter(play_data[play_data['NflId'] == play_data['NflIdRusher']]['X'], play_data[play_data['NflId'] == play_data['NflIdRusher']]['Y'], color='purple', s=100)
    axes[row, col].axvline(absolute_yard_line, color='yellow', linestyle='--', linewidth=2, label='Yard Line')
    axes[row, col].hlines(xmin=absolute_yard_line, xmax=yards, y=25, color='yellow', linestyle='--', linewidth=2, label='Yard Line')
    axes[row, col].set_title(f"Play {play_id}, Rusher: {play_data[play_data['NflId'] == play_data['NflIdRusher']]['Position'].iloc[0]}, Yards: {play_data['Yards'].iloc[0]}")
    axes[row, col].set_xlim(0, 120)
    axes[row, col].set_ylim(0, 53)
    axes[row, col].grid(False)

plt.tight_layout()
plt.show()
# List of unique PlayIds for the game
play_ids = game_data['PlayId'].unique()
print(play_ids)
# Plot the first few plays as an example
num_rows = (play_ids.shape[0] // 3) + 1  # Number of plays to plot
num_cols = 3
fig, axes = plt.subplots(num_rows, num_cols, figsize=(30, 126))

for idx, play_id in enumerate(play_ids):
    row = idx // 3
    col = idx % 3
    play_data = game_data[game_data['PlayId'] == play_id]
    yard_line = play_data['YardLine'].iloc[0]
    yards = play_data['Yards'].iloc[0]
    field_position = play_data['FieldPosition'].iloc[0]
    possesion_team = play_data['PossessionTeam'].iloc[0]
    home_team_abbr = play_data['HomeTeamAbbr'].iloc[0]
    if field_position == home_team_abbr:
      absolute_yard_line = 10 + 50 + (50-yard_line)
    else:
      absolute_yard_line = 10 + yard_line
    if (possesion_team == home_team_abbr):
      yards = absolute_yard_line - yards
    else:
      yards = absolute_yard_line + yards
    axes[row, col].imshow(image, extent=[-1, 121, -1, 54])
    axes[row, col].scatter(play_data[play_data['Team'] == 'away']['X'], play_data[play_data['Team'] == 'away']['Y'], c='red', s=100)
    axes[row, col].scatter(play_data[play_data['Team'] == 'home']['X'], play_data[play_data['Team'] == 'home']['Y'], c='blue', s=100)
    axes[row, col].scatter(play_data[play_data['NflId'] == play_data['NflIdRusher']]['X'], play_data[play_data['NflId'] == play_data['NflIdRusher']]['Y'], color='purple', s=100)
    axes[row, col].axvline(absolute_yard_line, color='yellow', linestyle='--', linewidth=2, label='Yard Line')
    axes[row, col].hlines(xmin=absolute_yard_line, xmax=yards, y=25, color='yellow', linestyle='--', linewidth=2, label='Yard Line')
    axes[row, col].set_title(f"Play {play_id}, Rusher: {play_data[play_data['NflId'] == play_data['NflIdRusher']]['Position'].iloc[0]}, Yards: {play_data['Yards'].iloc[0]}")
    axes[row, col].set_xlim(0, 120)
    axes[row, col].set_ylim(0, 53)
    axes[row, col].grid(False)

plt.tight_layout()
plt.show()

[20170907000118 20170907000139 20170907000189 20170907000345
 20170907000395 20170907000473 20170907000516 20170907000653
 20170907000680 20170907000801 20170907000917 20170907001004
 20170907001077 20170907001156 20170907001177 20170907001296
 20170907001355 20170907001376 20170907001443 20170907001488
 20170907001509 20170907001530 20170907001551 20170907001605
 20170907001664 20170907001715 20170907001736 20170907001819
 20170907001955 20170907002430 20170907002648 20170907002669
 20170907002829 20170907002900 20170907002961 20170907003138
 20170907003161 20170907003261 20170907003444 20170907003465
 20170907003507 20170907003635 20170907003874 20170907004025
 20170907004046 20170907004182 20170907004314 20170907004465
 20170907004486 20170907004622 20170907004660 20170907004721]

$No description has been provided for this image$

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